Abstract: Multiscale, mechanistic, and predictive models of stroke in sickle cell disease Abstract Sickle cell disease (SCD) is a genetic disease caused by one point mutation that changes one amino acid in one protein inside one type of cell. Despite the singularity of SCD, the mutation causes multi-factorial damage, pain, lower life expectancy, and, most devastatingly, pediatric strokes. Children with SCD are 200 times more likely to suffer a stroke than others: over 10% will stroke by the age of 20. This accelerated timeline challenges cardiovascular paradigms that plaque formation occurs over 30-40 years of arterial damage. Neither stroke mechanisms in SCD nor reasons a subset is particularly vulnerable are clear. Preventive therapies have substantial side effects, requiring better predictions of which SCD patients need intervention. I propose to develop an innovative personalized medicine strategy using experimental and clinical data to train a mathematical model predictive of sickle cell disease stroke risk for earlier intervention of pediatric patients. To achieve this, I propose a hypotheses-based project incorporating research findings from diverse biomedical subspecialties. Engineers have linked disturbed blood flow, endothelial dysfunction in arteries, and plaque localization. Stiff, sticky, sickled red blood cells disturb and block blood flow, and physically damage the vascular wall. In response, proteases are released that degrade the structural proteins in the arterial wall, cell proliferation follows, and luminal narrowing persists. All of this is exacerbated by chronic inflammation. To capture the complexity of SCD and its positive feedback loops, I propose to develop multi-scale kinetic models that incorporate proteolytic mechanistic insight from biochemical and biomechanical consequences of SCD at the cell and tissue level with predictive statistical models based on clinical data, novel biomarkers, and patient outcomes. My rigorous training in cell biology, biomedical engineering, and mathematical modeling developed an innovative viewpoint and toolbox to motivate new therapies, indicate immediate intervention, and reduce costs and deaths of SCD patients. Public Health Relevance: Sickle cell disease is a genetic disease that causes systemic, multi-factorial damage, pain, lower life expectancy, and, most devastatingly, strokes in 11% of children with the genetic mutation. The goals of this project are to develop an innovative personalized medicine strategy using experimental and clinical data to train a mathematical model predictive of sickle cell disease stroke risk for earlier intervention of pediatric patients.